Granted Patent B2

US 10,216,801 · App. 14/819,272 · Granted Feb 26, 2019

Abstract

Claims (86)

1. A computer-implemented method comprising:

by one or more hardware computer processors configured with specific computer executable instructions:

accessing one or more electronic data stores, the one or more electronic data stores storing a plurality of data entities and respective data entity attributes;

applying a clustering strategy to generate a data entity cluster by at least:

designating a seed data entity, from the plurality of data entities, as the data entity cluster;

accessing, based on the clustering strategy, one or more search protocols;

performing first growth of the data entity cluster by executing at least a first of the one or more search protocols on the one or more electronic data stores to identify one or more data entities related to the seed data entity;

adding the one or more data entities to the data entity cluster;

performing second growth of the data entity cluster by executing at least a second of the one or more search protocols on the one or more electronic data stores to identify one or more additional data entities related to the one or more added data entities, the second search protocol different than the first search protocol; and

adding the one or more additional data entities to the data entity cluster; and

storing the data entity cluster in at least one of the one or more electronic data stores.

2. The computer-implemented method of claim 1 , wherein executing at least the first of the one or more search protocols on the one or more electronic data stores to identify one or more data entities related to the seed data entity further comprises:

by the one or more hardware computer processors configured with specific computer executable instructions:

identifying at least one data entity attribute associated with the seed data entity; and

evaluating the plurality of data entities to determine the one or more data entities sharing the at least one data entity attribute with the seed data entity.

3. The computer-implemented method of claim 2 , wherein executing at least the first of the one or more search protocols on the one or more electronic data stores to identify one or more data entities related to the seed data entity further comprises:

by the one or more hardware computer processors configured with specific computer executable instructions:

applying a filter to the at least one data entity attribute associated with the seed data entity, the filter selected based on the clustering strategy.

4. The computer-implemented method of claim 1 further comprising:

by the one or more hardware computer processors configured with specific computer executable instructions:

comparing data entities associated with the data entity cluster to data entities associated with a second data entity cluster; and

in response to determining that at least one data entity associated with the data entity cluster shares an attribute with and/or is related to at least one data entity associated with the second data entity cluster, merging the data entity cluster and the second data entity cluster.

5. The computer-implemented method of claim 1 , wherein the first search protocol searches for data entities in a first electronic data store and the second search protocol searches for data entities in a second electronic data store.

6. The computer-implemented method of claim 1 , wherein the data entity cluster is iteratively generated by further:

by the one or more hardware computer processors configured with specific computer executable instructions:

executing at least a third of the one or more search protocols on the one or more electronic data stores to identify yet one or more additional data entities related to the one or more additional data entities; and

adding the yet one or more additional data entities to the data entity cluster.

7. The computer-implemented method of claim 1 further comprising:

by the one or more hardware computer processors configured with specific computer executable instructions:

causing a ranking score to be assigned to the data entity cluster; and

ordering a listing of the data entity cluster and other data entity clusters relative to a one another.

8. A computer-implemented method of accessing one or more electronic data sources, the method comprising:

by one or more hardware computer processors configured with specific computer executable instructions:

accessing one or more electronic data stores, the one or more electronic data stores storing:

a plurality of data entities and respective data entity attributes, and

a plurality of data entity clusters; and

causing access of a data entity cluster of the plurality of data entity clusters, wherein the data entity cluster is related to a clustering strategy, and wherein the data entity cluster has been iteratively generated by:

designating a seed data entity, from the plurality of data entities, as the data entity cluster;

accessing, based on the clustering strategy, one or more search protocols;

performing first growth of the data entity cluster by executing at least a first of the one or more search protocols on the one or more electronic data stores to identify one or more data entities related to the seed data entity;

adding the one or more data entities to the data entity cluster;

performing second growth of the data entity cluster by executing at least a second of the one or more search protocols on the one or more electronic data stores to identify one or more additional data entities related to the one or more added data entities, the second search protocol different than the first search protocol; and

adding the one or more additional data entities to the data entity cluster.

9. The computer-implemented method of claim 8 , wherein executing at least the first of the one or more search protocols on the one or more electronic data stores to identify one or more data entities related to the seed data entity further comprises:

by the one or more hardware computer processors configured with specific computer executable instructions:

identifying at least one data entity attribute associated with the seed data entity; and

evaluating the plurality of data entities to determine the one or more data entities sharing the at least one data entity attribute with the seed data entity.

10. The computer-implemented method of claim 9 , wherein executing at least the first of the one or more search protocols on the one or more electronic data stores to identify one or more data entities related to the seed data entity further comprises:

by the one or more hardware computer processors configured with specific computer executable instructions:

applying a filter to the at least one data entity attribute associated with the seed data entity, the filter selected based on the clustering strategy.

11. The computer-implemented method of claim 8 further comprising:

by the one or more hardware computer processors configured with specific computer executable instructions:

accessing, from the one or more electronic data stores, a scoring strategy for prioritizing the plurality of data entity clusters relative to one another;

for each particular data entity cluster of the plurality of data entity clusters:

evaluating, based on the scoring strategy, the particular data entity cluster; and

assigning, based on the evaluation, a score to the particular data entity cluster; and

ranking the plurality of data entity clusters according to the respective assigned scores.

12. The computer-implemented method of claim 11 , wherein the score assigned to each data entity cluster corresponds to an amount at risk.

13. The computer-implemented method of claim 11 , wherein assigning a score to the particular data entity cluster comprises:

by the one or more hardware computer processors configured with specific computer executable instructions:

determining a plurality of base scores for the particular data entity cluster;

determining, based on the plurality of base scores, an overall score for the particular data entity cluster; and

assigning the overall score to the particular data entity cluster.

14. The computer-implemented method of claim 11 further comprising:

by the one or more hardware computer processors configured with specific computer executable instructions:

generating a user interface configured to be rendered on a computing device; and

updating the user interface to include the listing of two or more of the plurality of data entity clusters according to the ranking.

15. The computer-implemented method of claim 8 , wherein the clustering strategy is associated with an investigation process.

16. The computer-implemented method of claim 8 further comprising:

by the one or more hardware computer processors configured with specific computer executable instructions:

generating a user interface configured to be rendered on a computing device.

17. The computer-implemented method of claim 16 further comprising:

by the one or more hardware computer processors configured with specific computer executable instructions:

receiving, via the user interface, a selection of at least one of:

the seed data entity selected from the plurality of data entities, or

a seed generation strategy by which the seed data entity is selected from the plurality of data entities.

18. The computer-implemented method of claim 16 further comprising:

by the one or more hardware computer processors configured with specific computer executable instructions:

receiving, via the user interface, a selection of the clustering strategy.

19. The computer-implemented method of claim 16 further comprising:

by the one or more hardware computer processors configured with specific computer executable instructions:

updating the user interface to include an indication of the data entity cluster; and

receiving, via the user interface, a selection of the data entity cluster.

20. The computer-implemented method of claim 8 further comprising:

by the one or more hardware computer processors configured with specific computer executable instructions:

applying the clustering strategy to iteratively generate the data entity cluster.

Assignments (8)

Cited By (2)

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